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psupertime: supervised pseudotime analysis for time-series single-cell RNA-seq data
Author(s) -
Will Macnair,
Revant Gupta,
Manfred Claassen
Publication year - 2022
Publication title -
bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btac227
Subject(s) - computer science , time series , classifier (uml) , benchmark (surveying) , series (stratigraphy) , artificial intelligence , identification (biology) , data mining , pattern recognition (psychology) , source code , machine learning , biology , paleontology , botany , geodesy , geography , operating system
Improvements in single-cell RNA-seq technologies mean that studies measuring multiple experimental conditions, such as time series, have become more common. At present, few computational methods exist to infer time series-specific transcriptome changes, and such studies have therefore typically used unsupervised pseudotime methods. While these methods identify cell subpopulations and the transitions between them, they are not appropriate for identifying the genes that vary coherently along the time series. In addition, the orderings they estimate are based only on the major sources of variation in the data, which may not correspond to the processes related to the time labels.

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